Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

Menesklou, Philipp; Sinn, Tabea; Nirschl, Hermann; Gleiß, Marco

doi:10.3390/min11070755

Cited by 14 publications

(10 citation statements)

References 25 publications

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“…The solid content rate at different rotational speeds was shown in Figure 17. It could be found that the separation performance was increased with the increase of the rotational speed, which was consistent with the results in the literatures 30,31 . Higher rotational speed resulted in increased centrifugal force, so the solid–liquid separation process could be completed in time in the drum.…”

Section: Resultssupporting

confidence: 90%

“…It could be found that the separation performance was increased with the increase of the rotational speed, which was consistent with the results in the literatures. 30,31 Higher rotational speed resulted in increased centrifugal force, so the solidliquid separation process could be completed in time in the drum. However, when the rotational speed was greater than 3000 rpm, the improvement in the separation performance was not obvious.…”

Section: Separation Performancementioning

confidence: 99%

See 1 more Smart Citation

Study on the structure of energy‐saving bend pipes in a decanter centrifuge

Fu,

Li,

Zhou

et al. 2023

Asia-Pacific J Chem Eng

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Decanter centrifuges are widely used in solid–liquid separation, and saving their energy consumption is very important. In this paper, a bend pipe installed at the end of the decanter centrifuge was designed to save energy, and the effect of different structural parameters of the energy‐saving bend pipes on the outlet velocity and the reaction torque was investigated. The results revealed that the outlet velocity and the reaction torque were reduced with the increase in the number of pipes, outlet area, and pipe length. Increasing the radial position had little effect on the outlet velocity, but increased the reaction torque. Decreasing the outlet area and increasing the radial position both weaken the separation performance of the decanter centrifuge. The best separation performance and reaction torque effect were achieved in a suitable structure. Furthermore, the turbulent intensity was reduced from 143% to 135% in the new machine installed with bend pipes. The energy‐saving efficiency dropped with the increase of the rotational speed and the value was 13.1% under 2500 rpm. The separation performance of the new machine was similar to that of the original machine, and the dewatering speed was increased with the increase of the axial position.

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Section: Resultssupporting

confidence: 90%

Section: Separation Performancementioning

confidence: 99%

Study on the structure of energy‐saving bend pipes in a decanter centrifuge

Fu,

Li,

Zhou

et al. 2023

Asia-Pacific J Chem Eng

View full text Add to dashboard Cite

show abstract

“…In contrast to these two models, grey box models utilize semi-empirical equations derived from the experimental data, balancing between complex and simple solutions [73,74]. Machine learning-assisted CFD simulation, which is a combination of black box and white box models, is referred to as grey box models [75].…”

Section: Applications Potentials and Hurdles Of Pemfc System Integrationmentioning

confidence: 99%

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

Kaiser,

Ahn,

Kim

et al. 2024

Processes

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For mitigating global warming, polymer electrolyte membrane fuel cells have become promising, clean, and sustainable alternatives to existing energy sources. To increase the energy density and efficiency of polymer electrolyte membrane fuel cells (PEMFC), a comprehensive numerical modeling approach that can adequately predict the multiphysics and performance relative to the actual test such as an acceptable depiction of the electrochemistry, mass/species transfer, thermal management, and water generation/transportation is required. However, existing models suffer from reliability issues due to their dependency on several assumptions made for the sake of modeling simplification, as well as poor choices and approximations in material characterization and electrochemical parameters. In this regard, data-driven machine learning models could provide the missing and more appropriate parameters in conventional computational fluid dynamics models. The purpose of the present overview is to explore the state of the art in computational fluid dynamics of individual components of the modeling of PEMFC, their issues and limitations, and how they can be significantly improved by hybrid modeling techniques integrating with machine learning approaches. Furthermore, a detailed future direction of the proposed solution related to PEMFC and its impact on the transportation sector is discussed.

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“…In addition, Yang proposed the use of the well-known grey-box modeling that is being used in various other fields as well [54][55][56][57][58]. A figure indicating a grey-box model is depicted in Figure 2.…”

Section: Current Digital Twin Applications In 3d Printing Technologiesmentioning

confidence: 99%

3D Printing and Implementation of Digital Twins: Current Trends and Limitations

Kantaros

Piromalis

Tsaramirsis

et al. 2021

ASI

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Fabricating objects with desired mechanical properties by utilizing 3D printing methods can be expensive and time-consuming, especially when based only on a trial-and-error test modus operandi. Digital twins (DT) can be proposed as a solution to understand, analyze and improve the fabricated item, service system or production line. However, the development of relevant DTs is still hampered by a number of factors, such as a lack of full understanding of the concept of DTs, their context and method of development. In addition, the connection between existing conventional systems and their data is under development. This work aims to summarize and review the current trends and limitations in DTs for additive manufacturing, in order to provide more insights for further research on DT systems.

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Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

Cited by 14 publications

References 25 publications

Study on the structure of energy‐saving bend pipes in a decanter centrifuge

Study on the structure of energy‐saving bend pipes in a decanter centrifuge

Towards Reliable Prediction of Performance for Polymer Electrolyte Membrane Fuel Cells via Machine Learning-Integrated Hybrid Numerical Simulations

3D Printing and Implementation of Digital Twins: Current Trends and Limitations

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